{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../datasets/Google.csv\")\n",
    "df.index = pd.DatetimeIndex(df['Date'].values)\n",
    "close = df[\"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def get_hhi(bet_ret):\n",
    "    if bet_ret.shape[0] <= 2:\n",
    "        return np.nan\n",
    "    wght = bet_ret / bet_ret.sum()\n",
    "    hhi = (wght ** 2).sum()\n",
    "    hhi = (hhi - bet_ret.shape[0] ** -1) / (1 - bet_ret.shape[0] ** -1)\n",
    "    return hhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000835603622536342 0.0013499951193839117 4.8223459418227205e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tom/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "ret = close.pct_change().dropna()\n",
    "hhi_pos = get_hhi(ret[ret >= 0])\n",
    "hhi_neg = get_hhi(ret[ret < 0])\n",
    "hhi_m = get_hhi(ret.groupby(pd.TimeGrouper(freq='M')).count())\n",
    "print(hhi_pos, hhi_neg, hhi_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeDD_TUW(series, dollars=False):\n",
    "    df = series.to_frame('pnl')\n",
    "    df['hwm'] = series.expanding().max()\n",
    "    # Lowest points with hwm\n",
    "    min_df = df.groupby('hwm').min().reset_index()\n",
    "    min_df.columns = ['hwm', 'min']\n",
    "    # Index is time for hwm\n",
    "    min_df.index = df['hwm'].drop_duplicates(keep='first').index\n",
    "    min_df = min_df[min_df['hwm'] > min_df['min']]\n",
    "    if dollars:\n",
    "        dd = min_df['hwm'] - min_df['min']\n",
    "    else:\n",
    "        dd = 1 - min_df['min'] / min_df['hwm']\n",
    "    tuw = ((min_df.index[1:] - min_df.index[:-1]) / np.timedelta64(1, 'Y')).values\n",
    "    tuw = pd.Series(tuw, index=min_df.index[:-1])\n",
    "    return dd, tuw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd, tuw = computeDD_TUW(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004-08-23    0.085832\n",
      "2004-09-20    0.012735\n",
      "2004-09-23    0.021189\n",
      "2004-09-29    0.011291\n",
      "2004-10-05    0.009323\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe350246c88>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe350205160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(dd.head())\n",
    "dd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004-08-23    0.076661\n",
      "2004-09-20    0.008214\n",
      "2004-09-23    0.016427\n",
      "2004-09-29    0.016427\n",
      "2004-10-05    0.005476\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3501cccf8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe3501b08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(tuw.head())\n",
    "tuw.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([0.002737907006988508, 0.008213721020965523, 0.002737907006988508,\n",
       "              0.002737907006988508, 0.002737907006988508, 0.002737907006988508,\n",
       "              0.008213721020965523, 0.002737907006988508, 0.002737907006988508,\n",
       "              0.002737907006988508,\n",
       "              ...\n",
       "              0.010951628027954031, 0.002737907006988508, 0.002737907006988508,\n",
       "              0.002737907006988508, 0.008213721020965523, 0.002737907006988508,\n",
       "              0.002737907006988508, 0.002737907006988508, 0.002737907006988508,\n",
       "              0.010951628027954031],\n",
       "             dtype='float64', length=3124)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(close.index[1:] - close.index[:-1]) / np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
